{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.io as sio\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools\n",
    "from collections import defaultdict\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats01s = sio.loadmat('16qubit_data/stats_0.mat')['stats01s']\n",
    "measure_fids = sio.loadmat('16qubit_data/measure_fids.mat')['measure_fids']"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# typical correction and get raw value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def findIndex(qNum, qidx):\n",
    "    order = 2**(qNum-qidx+1)*np.arange(0, 2**(qidx-1))\n",
    "    index0 = np.reshape(order, [len(order), 1]) + np.arange(0, 2**(qNum-qidx))\n",
    "    index0 = np.reshape(index0, 2**(qNum-1))\n",
    "    index1 = index0 + 2**(qNum-qidx)\n",
    "    return np.int32(index0), np.int32(index1)\n",
    "def nchoosek(qnum, n=1):\n",
    "    c = []\n",
    "    for i in itertools.combinations(range(qnum),n):\n",
    "        c.append(list(i))\n",
    "    return c\n",
    "qnum=16\n",
    "_basis = np.zeros([2**qnum,qnum])\n",
    "for i in range(qnum):\n",
    "    index0,index1 = findIndex(qnum,i+1)\n",
    "    _basis[index1,i] = 1\n",
    "ind_occ = nchoosek(qnum=qnum,n=qnum//2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "dim0 = stats01s.shape\n",
    "qnum=dim0[2]\n",
    "probs = []\n",
    "probIndexs = []\n",
    "binCoeff = 2**np.arange(dim0[2]-1, -1, -1)\n",
    "probIndexAll = np.arange(2**dim0[2])\n",
    "for ii in range(dim0[1]):\n",
    "    pCounter = np.zeros(2**dim0[2], dtype=int)\n",
    "    for jj in range(dim0[0]):\n",
    "        pCounter[np.sum(stats01s[jj,ii,:] * binCoeff)] += 1\n",
    "    probIndex = probIndexAll[pCounter>0]\n",
    "    prob = pCounter[pCounter>0]\n",
    "    probs.append(prob)\n",
    "    probIndexs.append(probIndex)\n",
    "probs=np.asarray(probs,dtype=object)\n",
    "probIndexs=np.asarray(probIndexs,dtype=object)\n",
    "probs_raw=np.zeros([dim0[1],2**qnum])\n",
    "for i in range(dim0[1]):\n",
    "    probs_raw[i,probIndexs[i]]=probs[i]/dim0[0]\n",
    "probs = probs_raw\n",
    "probIndexs = 0\n",
    "for ii in range(qnum):\n",
    "    f0 = measure_fids[ii][0]\n",
    "    f1 = measure_fids[ii][1]\n",
    "    corrMat = np.matrix([[f0, 1 - f1], [1 - f0, f1]])\n",
    "    corrMat = corrMat.I\n",
    "    indexP0, indexP1 = findIndex(qnum, ii+1)\n",
    "    probsCorr = np.copy(probs)\n",
    "    probsCorr[:,indexP0] = probs[:,indexP0]*corrMat[0, 0] + probs[:,indexP1] * corrMat[0, 1]\n",
    "    probsCorr[:,indexP1] = probs[:,indexP0]*corrMat[1, 0] + probs[:,indexP1] * corrMat[1, 1]\n",
    "    probs = probsCorr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "ini_state = np.sum(stats01s[:,0,:],axis=0)/dim0[0]\n",
    "ini_state = np.array([1 if i>0.5 else 0 for i in ini_state])\n",
    "hamming_raw = np.zeros([dim0[1],qnum+1])\n",
    "for i in range(dim0[0]):\n",
    "    hamming_raw_tmp = np.array(np.sum(np.abs(stats01s[i,:,:]-ini_state),axis=1),dtype=int)\n",
    "    for j in range(dim0[1]):\n",
    "        hamming_raw[j,hamming_raw_tmp[j]]+=1\n",
    "hamming_raw = hamming_raw/dim0[0]\n",
    "psi_ini = np.array(ini_state)\n",
    "psis_temp=np.array(np.sum(np.abs(_basis-psi_ini),axis=1),dtype=np.int32)\n",
    "hammingdex=[np.where(psis_temp==i)[0] for i in range(qnum+1)]\n",
    "hamming_corr=np.array([np.sum(probs[:,hammingdex[i]],axis=1) for i in range(qnum+1)]).transpose(1,0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# sw_rr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "meas_mats, meas_mats_inv = [], []\n",
    "for qubit in range(qnum):\n",
    "    meas_mat = np.array([[\n",
    "        measure_fids[qubit][0], 1-measure_fids[qubit][1]],\n",
    "        [1-measure_fids[qubit][0], measure_fids[qubit][1]]\n",
    "    ])\n",
    "    meas_mats.append(meas_mat)\n",
    "    meas_mats_inv.append(np.linalg.inv(meas_mat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "i=5\n",
    "stats_num =dim0[0]\n",
    "stats_counts = {}\n",
    "stats_strs_tmp = []\n",
    "stats_strs_type_tmp = []\n",
    "for j in range(stats_num):\n",
    "    stat_str = ''.join([str(ii) for ii in stats01s[j,i,:]])\n",
    "    stats_strs_tmp.append(stat_str)\n",
    "    if stat_str not in stats_strs_type_tmp:\n",
    "        stats_strs_type_tmp.append(stat_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats_counts = dict(zip(stats_strs_type_tmp, [stats_strs_tmp.count(stat_str) for stat_str in stats_strs_type_tmp]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 387/387 [03:07<00:00,  2.06it/s]\n"
     ]
    }
   ],
   "source": [
    "def transform_(matrix, local_basis):\n",
    "    local_vec = np.zeros(2)\n",
    "    local_vec[local_basis] = 1\n",
    "    local_vec = matrix @ local_vec\n",
    "    return local_vec  # new_basis_value\n",
    "\n",
    "rm_prob = defaultdict(float)\n",
    "\n",
    "for basis, prob in tqdm(stats_counts.items()):\n",
    "    basis = [int(c) for c in basis]\n",
    "    local_vecs = []\n",
    "    for qubit in range(qnum):\n",
    "        local_vecs.append(transform_(meas_mats_inv[qubit], basis[qubit]))\n",
    "\n",
    "    now_basis_values = [\n",
    "        [[local_basis], local_value*prob]\n",
    "        for local_basis, local_value in enumerate(local_vecs[0])\n",
    "    ]\n",
    "\n",
    "    for qubit in range(1, qnum):\n",
    "        next_basis_values = []\n",
    "        for local_basis, local_value in enumerate(local_vecs[qubit]):\n",
    "            for basis, value in now_basis_values:\n",
    "                basis = list(basis)\n",
    "                next_value = value * local_value\n",
    "\n",
    "                if np.abs(next_value) < 1e-10:  # 1e-2 ** n_qubits:\n",
    "                    continue\n",
    "\n",
    "                basis.append(local_basis)  # 可以改成进制的\n",
    "                next_basis_values.append([basis, next_value])\n",
    "\n",
    "        now_basis_values = next_basis_values\n",
    "\n",
    "    for basis, value in now_basis_values:\n",
    "        basis = np.array(basis)\n",
    "        rm_prob[''.join(basis.astype(np.str_))] += value\n",
    "\n",
    "    new_rm_prob = defaultdict(float)\n",
    "    for basis, value in rm_prob.items():\n",
    "        if np.abs(value) < 1e-10:  # 1e-2 ** n_qubits:\n",
    "            continue\n",
    "        new_rm_prob[basis] = value\n",
    "    rm_prob = new_rm_prob\n",
    "\n",
    "sum_prob = sum(rm_prob.values())\n",
    "rm_prob = {\n",
    "    basis: value / sum_prob\n",
    "    for basis, value in rm_prob.items()\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "hamming_sw_rr=np.zeros([qnum+1])\n",
    "ini_state_str = ''.join(['1' if ini_i == 1 else '0' for ini_i in ini_state])\n",
    "for q_str in rm_prob.keys():\n",
    "    hamming_sw_rr[sum(c1 != c2 for c1, c2 in zip(q_str, ini_state_str))]+=rm_prob[q_str]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'hamming probs')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "LABEL_COLORS_1 = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink']\n",
    "tlist = np.arange(dim0[1])\n",
    "select_t = [5,30,100]\n",
    "plt.figure(figsize=(9,6),dpi=100)\n",
    "for color_i,i in enumerate(select_t):\n",
    "    plt.plot(np.arange(0,qnum+1,1),hamming_raw[i,:],'o--',color=LABEL_COLORS_1[color_i],\n",
    "            markerfacecolor='none',label=f'Raw t = {tlist[i]}')\n",
    "    plt.plot(np.arange(0,qnum+1,1),hamming_corr[i,:],'*-',\n",
    "            color=LABEL_COLORS_1[color_i],\n",
    "            markerfacecolor='none',label=f'corrected t = {tlist[i]}')\n",
    "plt.plot(np.arange(0,qnum+1,1),hamming_sw_rr,'D-',alpha=0.5,\n",
    "            color=LABEL_COLORS_1[color_i],\n",
    "            markerfacecolor='none',label=f'sw corrected t = {tlist[i]}')\n",
    "plt.legend(ncol=len(select_t))\n",
    "plt.xlabel('hamming distance')\n",
    "plt.ylabel('hamming probs')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
